Abstract:

Entity resolution (ER), comprising record linkage and de-duplication, is the process of merging noisy databases in the absence of unique identifiers, with the goal of removing duplicate entities. One major challenge of analysis with linked data is identifying a representative record among determined matches to pass to an inferential or predictive task, referred to as the downstream task. Additionally, incorporating uncertainty from ER in the downstream task is critical to ensure proper inference. In this talk, we present five fully unsupervised methods to choose representative (or canonical) records from linked data (referred to as canonicalization), including a fully Bayesian approach which propagates the error from linkage through to the downstream inference. This multi-stage approach is evaluated on three simulated data sets and one application — determining the relationship between demographic information and party affiliation in voter registration data from the North Carolina State Board of Elections. We first perform Bayesian ER and evaluate our proposed methods for canonicalization before considering the downstream tasks of linear and logistic regression. Bayesian canonicalization methods are empirically shown to improve downstream inference in both settings.

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Topic: Colloquia: Department of Statistics and Actuarial Science, The University of Iowa

Time: September 16, 2021 03:15 PM Central Time (US and Canada)

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Meeting ID: 989 2869 3758

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